Efficient recycling of waste into reusable raw materials is one of the significant efforts we must take to stop global warming and the over-exploitation of natural resources.
The environmental benefits of recycling are clear. Recycling conserves natural resources and reduces greenhouse gases and pollution, and the use of fossil fuels in energy production. It reduces energy consumption by about 70% for plastics, 60% for steel, 40% for paper, and 30% for glass.
A significant value lies in reusable material. However, we are still far away from our recycling targets. Most of the collected waste is still used for energy production and burnt in power plants – not reused. Price is often a factor in low recycling rates, as it is often cheaper to produce new products from raw materials than recycled materials.
To make recycling not only ecologically but also economically viable, reusing materials needs to be cheaper and easier than using virgin materials. With proper material handling methods, different materials can be efficiently recycled and turned into profit. This is where hyperspectral imaging can make a difference.
A significant value lies in reusable material. However, we are still far away from our recycling targets. Most of the collected waste is still used for energy production and burnt in power plants – not reused. Price is often a factor in low recycling rates, as it is often cheaper to produce new products from raw materials than recycled materials.
To make recycling not only ecologically but also economically viable, reusing materials needs to be cheaper and easier than using virgin materials. With proper material handling methods, different materials can be efficiently recycled and turned into profit. This is where hyperspectral imaging can make a difference.
CURRENT CHALLENGES IN EFFICIENT RECYCLING
A typical waste management process includes the collection of waste in a recovery facility, segregation into different waste fractions, cleaning, and final classification into materials that are placed in landfills, burned, or recycled based on the type and purity.
The sorting process is a critical step in recycling. Better sorting accuracy means better separation of different grades of material, which results in higher value recovery. A typical sorting process is based on a mix of techniques and cannot rely on just one detection technology. The detection technology used often limits the types and the amount of the collected material that can be sorted.
Most of the recycling plants use different technologies from bar code readers and RGB cameras to X-Ray and Eddy current systems. While they are capable technologies to a certain extent, they are not perfect solutions as their capability to identify the material is limited.
For example, if a plastic bottle is missing the barcode, it is not possible to detect if it is PET or HDPE. Eddy-current detectors can sort out conductive metals but not separate plastics or pulp. RGB cameras can sort bottles into transparent, black, and coloured but cannot distinguish one plastic type from another.
When the recycled portion is not pure enough for reuse, we lose recyclable material to landfill or energy production. The poor sorting result also results in lost profits, which makes recycling unprofitable and dependent on public support.
Different waste streams require different detection and processing methods to be recycled efficiently and current recycling methods are not flexible, efficient, and informative enough to tackle the challenge.
To make up for inadequate detection technologies, human labor is still used. Sorting waste by hand is slow, inaccurate, expensive, and dangerous, and separating different plastic types from each other remains impossible because the human eye cannot tell them apart.
To work efficiently, profitably, and safely recycling plants must have sensors capable of separating different materials reliably and with high purity. Hyperspectral imaging offers a powerful technology for accurate and sustainable waste recycling.
HOW HYPERSPECTRAL CAMERAS CAN IMPROVE RECYCLING EFFICIENCY?
Hyperspectral cameras can differentiate materials accurately and reliably based on their chemical composition. They measure and analyse the spectrum of light reflected from or transmitted through the material. When measuring the spectrum beyond the visible region called near-infrared (NIR), we see that chemically different materials have unique spectra.
Multispectral technology has improved the situation; however, it has its limitations. Multispectral cameras acquire spectral data typically with one to three, or in some cameras, a maximum of 8 spectral bands, meaning that in each sorting location, it identifies only a few basic materials. The purity of the result is also often limited as there are interfering factors in the material stream. (Read more about the difference between multispectral and hyperspectral cameras)
The use of hyperspectral imaging in waste sorting has been restricted by the insufficient performance of hyperspectral cameras in terms of speed, spatial resolution, ruggedness, connectivity, and high cost – until recent years.
The recent development has improved both speed and resolution of hyperspectral cameras, while their implementation cost now meets the ROI criteria of commercial solutions. Furthermore, the algorithms and solutions for the real-time processing of a large amount of data produced by hyperspectral cameras are now available.
For in-line sorting applications, a line-scan hyperspectral camera is the only practical and properly working solution, as it captures the entire spectral data of the full material stream from each pixel in the line precisely at the same time with a single scan.
A line-scan (push-broom) hyperspectral camera can be installed on existing and new sorting lines with proper illumination and a real-time data processing solution like any line-scan camera. The material identification result, pixel by pixel, is available through a standard interface to commercial machine vision systems. The results can then be used to control the air nozzles or picking robots.
A hyperspectral camera solution provides superior performance and several benefits in various waste treatment processes over conventional sensor technologies, as summarised in Table 1.
Table 1. Added value by hyperspectral imaging in sorting different types of waste streams
When used together with other technologies, hyperspectral cameras increase sorting accuracy by providing precise information on material type. The latest generation of hyperspectral cameras can increase the purity of recycled materials by close to 100%. Increasing the purity of recycled plastic by even a few percent can double its value. Extracting more recyclable material also means that we are disposing of less waste in landfills.
Compared to a multi-spectral camera with fixed spectral bands, the hyperspectral camera is flexible and can adapt to sorting various waste streams. It can also adopt new sorting algorithms when they become available.
Specim Hyperspectral Imaging Cameras are available in the UK and Ireland through Quantum Design UK and Ireland. Learn more at qd-uki.co.uk
